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SAM: speech-aware applications in medicine to support structured data entry

A K Wormek1, J Ingenerf, H F Orthner

  • 1GSF-National Research Center for Environment and Health, Medis., Institute of Medical Informatics and Health Services Research, Neuherberg, Germany.

Proceedings : a Conference of the American Medical Informatics Association. AMIA Fall Symposium
|January 1, 1997
PubMed
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Speech recognition technology is advancing for clinical use. Adapting systems like SAM to specific medical needs, such as pathology and diabetes care, improves physician support and data entry.

Area of Science:

  • Medical Informatics
  • Computer-Assisted Diagnostics

Background:

  • Advancements in speech recognition technology are increasing medical interest in clinical applications.
  • Key factors for acceptance include speed, large vocabularies, accuracy, continuous recognition, and speaker independence.

Purpose of the Study:

  • To develop a speech-aware core component (SAM) for integrating diverse speech recognition engines.
  • To support physicians in routine clinical care through speech-aware applications.

Main Methods:

  • Development of SAM, a core component connecting to various speech recognition engines.
  • Extension of SAM within the STAMP project for pathology, combining structured data entry with speech recognition.
  • Integration of a speech-aware application in diabetes care with a terminology server for controlled vocabulary.

Related Experiment Videos

Main Results:

  • SAM enables connection to different speech recognition engines.
  • The STAMP project demonstrates combined structured data entry and speech recognition for pathology reporting.
  • A diabetes care application utilizes a terminology server for enhanced speech recognition.

Conclusions:

  • Adapting speech recognition to specific medical applications offers significant benefits.
  • Speech-aware components like SAM can enhance clinical workflows and physician support.
  • Integration with terminology servers improves the accuracy and utility of medical speech recognition.